Physical Annotation for Automated Optical Inspection: A Concept for In-Situ, Pointer-Based Training Data Generation

This paper introduces a novel physical annotation system that is designed to generate training data for automated optical inspection. The system uses pointer-based, in-situ interaction to transfer the valuable expertise of trained inspection personnel directly into a machine learning training pipeline. Unlike conventional screen-based annotation methods, our system allows annotation directly on the physical object, providing a more intuitive and efficient way to label data. The core technology uses calibrated, tracked pointers to accurately record user input and convert these spatial interactions into standardised annotation formats compatible with open-source software. A simple projector-based interface also projects visual guidance onto the object to assist users during the annotation process, ensuring greater accuracy and consistency. The proposed concept bridges the gap between human expertise and automated data generation. It enables non-IT experts to contribute to the ML training pipeline. Preliminary evaluation results confirm the feasibility of capturing detailed annotation trajectories and demonstrate that integration with CVAT streamlines the workflow for subsequent ML tasks. This paper details the system architecture, calibration procedures, and interface design, and discusses the concept's potential contribution to future ML-based automated optical inspection.